Abstract
With e-commerce gradually replacing traditional retailers and the ability to analyze consumer behavioral data to recommend related products, the importance of recommendation systems has been increasing. Although there have been several cross-domain recommendation systems, they often require a significant quantity of co-user training data. Therefore, when a recommendation model is trained, its recommendation results may be biased toward co-users, leading to biased recommendation results. This becomes even more problematic when new users join the system, and the overall cross-domain recommendation model needs to be retrained, resulting in the loss of previously acquired knowledge. Our study mitigates these two problems by using task-agnostic meta learning with incremental learning. Our experimental results show that the resultant distribution is more diverse than existing cross-domain recommendation systems (i.e., not biased toward co-users), thus increasing the variety of recommendation results. The performance accuracy improved by at least 10.46% for recall, 5.75% for precision, and 33.87% for normalized discounted cumulative gain.
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Acknowledgements
This research is funded by the Ministry of Science and Technology (Project No. MOST109-2221-E155-029-MY2、MOST109-2221-E-011-103-MY2). We would like to thank Uni-edit (www.uni-edit.net) for editing and proofreading this manuscript.
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Shih, CW., Lu, CH. & Hwang, IS. Cross-domain incremental recommendation system based on meta learning. J Ambient Intell Human Comput 14, 16563–16574 (2023). https://doi.org/10.1007/s12652-022-03911-z
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DOI: https://doi.org/10.1007/s12652-022-03911-z